Evaluating Differences Between Observers surveying for rails at night on ATVs

Auriel M.V. Fournier

Introduction

Rails are very difficult to detect, which makes surveying for them, and assessing the success of those surveys, very challenging. Rails are difficult to detect because they don’t vocalize regularly in the fall, and do not respond to vocalizations, and their behavior has them dense in vegetation most of the time. They are also reluctant to flush out of the vegetation until you are very close to them. This is one of the reasons they are the least studied group of birds in North America and why there are no standard survey methods for them outside of the breeding season (Conway 2012).

In 2013 and 2014 my project did double surveys each night, in which a wetland impoundment was surveyed two times, by two different people, in a short period of time (3 hours). Since rails are active during the day other then responding to our presence in the wetland (for which I have telemetry data to support they only move 3-10 meters) we are assuming that they are sleeping or still the rest of the night. As a result we hope that these two surveys are surveying the same birds in roughly the same locations. I propose using point pattern analysis to examine the differences in marked point patterns from 2013 and 2014. I will look at distance from the line and how it impacts detection along with differences in the patterns of points between the two observers.

Methods

I have a marked point patterns of observations of Sora (Porzana carolina) on 13 different public properties in 45 different wetland units across 6 months of surveys (3 months in each year). I will summarize the differences in pattern at the wetland level, as that is the unit of interest to my project as a whole and is the unit at which we consider the survey to be completed.

Across that time period we had four different observers (four in 2013 and two returning in 2014). For the purposes of this project we are just going to look at 2014, since there are only two observers so its simpler. If these patterns work for two observers I will expand them to the four from 2013 in the future.

library(ggplot2)
library(rgdal)
library(gridExtra)
library(spatstat)

First I subsetted out just the 2014 data and removed four of the sites because they had very few observations and were creating very high distance values. Then I reprojected it from lat/long to UTM.

setwd("C:/Users/avanderlaar/Documents/GitHub/data")
dat <- read.csv("all_birds.csv")
dat <- na.omit(dat)
dat <- dat[dat$species=="sora",]
## I am removing these three sites because they have very few points, and were causing most, but not all of the issues with outliers. These four sites are Ten Mile Pond Conservation Area (tmpca), Otter Slough Conservation Area (osca), Clarence Cannon National Wildlife Refuge (ccnwr) and Ted Shanks Conservation Area (tsca)
dat <- dat[dat$canwr!="tmpca"&dat$canwr!="osca"&dat$canwr!="ccnwr"&dat$canwr!="tsca",]

## reprojecting the points into utm 
utm <- as.data.frame(project(cbind(dat$long, dat$lat), "+proj=utm +zone=15 ellps=WGS84"))
colnames(utm) <- c("utm_w","utm_n")

dat <- cbind(dat, utm)
# taking only the 2014 points
dat4 <- dat[dat$year==2014,]

#figuring out how many unique survey days we have. jdate = julian date
jdate4 <- unique(dat4$jdate)

Then I created a list where each object in the list is a data frame of just the observations from that day.

# creating list to put each days points into seperately. 
list4 <- list()
## so this takes each day adn makes a list with each unique date being a level in the lsit
# removes days in which only one observer saw birds
for(i in 1:length(jdate4)){
  dat <- dat4[dat4$jdate==jdate4[i],]
  if (length(unique(dat$obs))>1){
  list4[[i]] <- dat}
}

## 

Then I wrote a for loop to use the crossdist function from the spatstat package to calculate the distnce from each point to every other point in another set of points, and vise versa. These are then rbind-ed together into one master dataset.

dist4 <- list()
newdf <- list()

dista <- list()
newdfa <- list()
for (i in c(1:length(list4))){
  # takes one element out of the list and makes it a data frame
  df <- as.data.frame(list4[[i]])
  if (nrow(df)>1){
    # splits the points from one night into two point patterns, one for me (A) and one for Nick (N)
    a <- df[df$obs=="N",]
    b <- df[df$obs=="A",]
    # figures out the distance between all the a points and the n points
    cdf <- crossdist(a$utm_w, a$utm_n, b$utm_w, b$utm_n)
    # finds the shortest distance for each point (row)
    c <- apply(cdf, 1, min)
    #figures out the distance between all the n points and the a points
    cdf2 <- crossdist(b$utm_w, b$utm_n, a$utm_w, a$utm_n)
    # finds the shortest distance for each point (row)
    d <- apply(cdf2, 1, min)
    # cbinding together the distances with their respective points, then stacking the points together with rbind into a new data frame for that particular jdate, and putting it in it's own object in this new list. 
    newdfa[[i]] <- rbind(cbind(a, dist=c),cbind(b,dist=d))
  }
  }
  
# binding together all the objects from the above for loop
dist <- do.call(rbind, newdfa)
# cutting out the outliers (those over 1000 meters, which are all instsances where the nearest point was in an entirely different survey unit)
dist <- dist[dist$dist<=1000,]

ggplot()+
  geom_histogram(data=dist, aes(x=dist), binwidth=10)+
  facet_grid(canwr ~ .)

So something weird is happening at Swan Lake NWR (slnwr) but the rest seem…vaguely normally distributed.

model <- lm(data=dist, dist ~ canwr)
summary(model)

## 
## Call:
## lm(formula = dist ~ canwr, data = dist)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -347.99  -87.30  -35.09   65.18  898.50 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   220.51      12.92  17.066  < 2e-16 ***
## canwrdcca    -113.41      24.58  -4.614 4.51e-06 ***
## canwrfgca     -96.09      25.26  -3.805 0.000151 ***
## canwrnvca    -127.87      17.49  -7.309 5.76e-13 ***
## canwrscnwr   -138.28      23.98  -5.767 1.10e-08 ***
## canwrslnwr    130.20      18.58   7.007 4.64e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 191.6 on 936 degrees of freedom
## Multiple R-squared:  0.224,  Adjusted R-squared:  0.2199 
## F-statistic: 54.04 on 5 and 936 DF,  p-value: < 2.2e-16

In 2013 and 2014 we put out radio transmitters on rails and tracked them in response to the ATVs. We never observed a rail moving more then 10 meters from its original location in response to an ATV. Based on our efforts catching rails at night we know that once they fly/or run from an ATV they typicall stop and stay where they end up. At night they are sleeping, so they likely return to sleeping after we distrub them. Based on thise I do not think they are actively moving around the impoundment during the night, atleast not large distances.